QHTD Framework: Integrating CALPHAD and Decision-Support Tools for Quantitative Alloy and Process Design

• QHTD provides a four-stage decision workflow integrating CALPHAD, diffusion kinetics, and property correlations into auditable alloy and process design. • Interpretive bands are derived using an entropy–TOPSIS MCDM framework applied in two modes (three-criterion and two-criterion) to transparently handle missing values. • A Fuzzy AHP method demonstration is provided for checklist weighting, with consistency ratios below 0.1 for the illustrative comparison matrices. • Three-mode sensitivity analysis spanning composition, temperature, and CALPHAD database variation quantifies process robustness. • Validation against published experimental data for eight steel grades, supported by leave-one-out analysis, is presented as a proof of concept. • A minimum viable implementation pathway with pre-calculated index values for twenty common steels enables partial adoption without CALPHAD access. • Ten reconstructed scenarios are presented as worked examples to demonstrate practical utility. • QHTD is complementary to the HT-PASS/Ψ_MPPT framework: QHTD targets offline auditability; HT-PASS targets online control. • Limitations regarding generalizability beyond carbon and low-alloy steels are explicitly documented. Heat treatment and alloy design remain dominated by qualitative rules despite mature computational thermodynamics. This article presents the Quantitative Heat Treatment Design (QHTD) Framework—a methodological framework that provides a four-stage decision workflow integrating CALPHAD thermodynamics, diffusion kinetics, and property correlations with structured documentation. Stage 1 locates alloy composition via the Thermodynamic Design Index (TDI). Stage 2 assesses process robustness via the Phase Stability Margin (PSM). Stage 3 navigates process–property space via Heat Treatment Maps (HTM). Stage 4 filters through kinetic feasibility via the Thermodynamic–Kinetic Coupling index (TCK), three-mode sensitivity analysis, and validation against published experimental data for eight steel grades. Supporting tools include a seven-field passport (TDP-PASS), a Thermodynamic Process Balance Sheet (TPBS) operating as a structured communication framework with an explicit unit-heterogeneity caveat, and a twelve-item checklist (QHTD-CHECK) mapped to ISO 9001:2015 clauses. Interpretive bands are derived using a multi-criteria decision-making (MCDM) framework that combines entropy weighting with TOPSIS ranking. The MCDM framework is fully reproducible and is applied in two modes—a three-criterion mode (TDI, PSM, TCK) for the subset of processes where all three criteria are defined, and a two-criterion mode (TDI, PSM) for the full dataset—to transparently handle missing values. Checklist item weights are established through a Fuzzy AHP method demonstration, with consistency ratios below 0.1 for the illustrative comparison matrices. The framework is positioned against ICME, MGI, and the complementary HT-PASS/Ψ_MPPT framework. Integration pathways with frontier computational methods—including physics-informed machine learning, CALPHAD-based Bayesian optimization, active learning, physics-informed neural networks, and morphology-aware property prediction—are specified. A minimum implementation pathway with pre-calculated index values for twenty common steels enables partial adoption without CALPHAD access. Ten reconstructed scenarios illustrate the framework's application. Limitations—including limited validation scope and the need for independent validation—are explicitly documented. The Quantitative Heat Treatment Design (QHTD) Framework — a four-stage decision workflow integrating CALPHAD thermodynamics, diffusion kinetics, and property correlations with structured documentation tools. Stage 1: Thermodynamic Design Index (TDI) locates alloy composition. Stage 2: Phase Stability Margin (PSM) assesses process robustness. Stage 3: Heat Treatment Maps (HTM) navigate process–property space. Stage 4: Kinetic feasibility checking via TCK, three-mode sensitivity analysis, and validation against published data. Supporting tools: seven-field documentation passport (TDP-PASS), Process Balance Sheet (TPBS) as a structured communication framework with an explicit unit-heterogeneity caveat, twelve-item checklist (QHTD-CHECK) with Fuzzy AHP weighting. Interpretive bands derived via entropy–TOPSIS MCDM framework applied in two modes. Integration pathways: PIML-CCT prediction, CALPHAD-based Bayesian optimization, active learning for phase diagrams, physics-informed neural networks for hardness, and morphology-aware property prediction. Positioned against ICME, MGI, ISO 9001, and HT-PASS/Ψ_MPPT. Minimum viable implementation with pre-calculated values for twenty steels. Note: A high-resolution image file must be prepared separately for submission.